{"id":331174,"date":"2025-12-17T01:59:54","date_gmt":"2025-12-16T15:59:54","guid":{"rendered":"https:\/\/science.nasa.gov\/missions\/landsat\/maintaining-the-gold-standard-the-future-of-landsat-calibration-and-validation\/"},"modified":"2025-12-17T01:59:54","modified_gmt":"2025-12-16T15:59:54","slug":"maintaining-the-gold-standard-the-future-of-landsat-calibration-and-validation","status":"publish","type":"post","link":"https:\/\/www.vibewire.com.au\/?p=331174","title":{"rendered":"Maintaining the Gold Standard: The Future of Landsat Calibration and Validation"},"content":{"rendered":"<p>By <a href=\"https:\/\/science.gsfc.nasa.gov\/earth\/biosphere\/bio\/amit.a.angal\" rel=\"noopener\">Amit Angal, Senior Engineer at Goddard Space Flight Center<\/a><\/p>\n<p>The <a href=\"https:\/\/science.nasa.gov\/mission\/landsat\/calibration-validation\/\" rel=\"noopener\">Landsat Calibration and Validation<\/a> (Cal\/Val) group helps uphold Landsat\u2019s reputation as the gold standard of satellite imagery. They ensure that the <a href=\"https:\/\/science.nasa.gov\/mission\/landsat\/oli\/\" rel=\"noopener\">Operational Land Imager (OLI)<\/a> and the <a href=\"https:\/\/science.nasa.gov\/mission\/landsat\/tirs\/\" rel=\"noopener\">Thermal Infrared Sensor (TIRS)<\/a> aboard Landsats 8 and 9 provide high-quality scientific measurements to users around the world. In 2025, the Cal\/Val group contributed over 60 pages to the second edition of \u201c<a href=\"https:\/\/www.sciencedirect.com\/science\/chapter\/referencework\/abs\/pii\/B9780443132209000457?via%3Dihub\" rel=\"noopener\">Comprehensive Remote Sensing<\/a>\u201d (Kaita et. al, 2026), organizing content from NASA, USGS, academia, and industry scientists. Cal\/Val support staff authored multiple sections, including a summary of results from Landsat 9 and of the evolution of spectral, spatial, and radiometric characteristics throughout the Landsat missions.<\/p>\n<div id=\"\" class=\"hds-media hds-module wp-block-image\">\n<div class=\"margin-left-auto margin-right-auto nasa-block-align-inline\">\n<div class=\"hds-media-wrapper margin-left-auto margin-right-auto\">\n<figure class=\"hds-media-inner hds-cover-wrapper hds-media-ratio-cover \"><a href=\"https:\/\/assets.science.nasa.gov\/dynamicimage\/assets\/science\/missions\/landsat\/data-pages\/Railroad-Valley-Nevada.png?w=2678&#038;h=2720&#038;%23038;fit=clip&#038;%23038;crop=faces%2Cfocalpoint\" rel=\"noopener\"><img fetchpriority=\"high\" decoding=\"async\" width=\"2678\" height=\"2720\" src=\"https:\/\/assets.science.nasa.gov\/dynamicimage\/assets\/science\/missions\/landsat\/data-pages\/Railroad-Valley-Nevada.png?w=2678&#038;h=2720&#038;%23038;fit=clip&#038;%23038;crop=faces%2Cfocalpoint\" class=\"attachment-2048x2048 size-2048x2048\" alt=\"A Landsat image of Railroad Valley Playa calibration site. The sandy area of the site appears white and tan and the rest of the image is varying shades of brown, red, and dark green.\" style=\"transform: scale(2); transform-origin: 41% 77%; object-position: 41% 77%; object-fit: cover;\" block_context=\"nasa-block\" loading=\"eager\" srcset=\"https:\/\/assets.science.nasa.gov\/dynamicimage\/assets\/science\/missions\/landsat\/data-pages\/Railroad-Valley-Nevada.png?w=2678&#038;h=2720&#038;fit=crop&#038;crop=faces%2Cfocalpoint 2678w, https:\/\/assets.science.nasa.gov\/dynamicimage\/assets\/science\/missions\/landsat\/data-pages\/Railroad-Valley-Nevada.png?w=295&#038;h=300&#038;fit=crop&#038;crop=faces%2Cfocalpoint 295w, https:\/\/assets.science.nasa.gov\/dynamicimage\/assets\/science\/missions\/landsat\/data-pages\/Railroad-Valley-Nevada.png?w=768&#038;h=780&#038;fit=crop&#038;crop=faces%2Cfocalpoint 768w, https:\/\/assets.science.nasa.gov\/dynamicimage\/assets\/science\/missions\/landsat\/data-pages\/Railroad-Valley-Nevada.png?w=1008&#038;h=1024&#038;fit=crop&#038;crop=faces%2Cfocalpoint 1008w, https:\/\/assets.science.nasa.gov\/dynamicimage\/assets\/science\/missions\/landsat\/data-pages\/Railroad-Valley-Nevada.png?w=1512&#038;h=1536&#038;fit=crop&#038;crop=faces%2Cfocalpoint 1512w, https:\/\/assets.science.nasa.gov\/dynamicimage\/assets\/science\/missions\/landsat\/data-pages\/Railroad-Valley-Nevada.png?w=2016&#038;h=2048&#038;fit=crop&#038;crop=faces%2Cfocalpoint 2016w, https:\/\/assets.science.nasa.gov\/dynamicimage\/assets\/science\/missions\/landsat\/data-pages\/Railroad-Valley-Nevada.png?w=50&#038;h=51&#038;fit=crop&#038;crop=faces%2Cfocalpoint 50w, https:\/\/assets.science.nasa.gov\/dynamicimage\/assets\/science\/missions\/landsat\/data-pages\/Railroad-Valley-Nevada.png?w=394&#038;h=400&#038;fit=crop&#038;crop=faces%2Cfocalpoint 394w, https:\/\/assets.science.nasa.gov\/dynamicimage\/assets\/science\/missions\/landsat\/data-pages\/Railroad-Valley-Nevada.png?w=591&#038;h=600&#038;fit=crop&#038;crop=faces%2Cfocalpoint 591w, https:\/\/assets.science.nasa.gov\/dynamicimage\/assets\/science\/missions\/landsat\/data-pages\/Railroad-Valley-Nevada.png?w=886&#038;h=900&#038;fit=crop&#038;crop=faces%2Cfocalpoint 886w, https:\/\/assets.science.nasa.gov\/dynamicimage\/assets\/science\/missions\/landsat\/data-pages\/Railroad-Valley-Nevada.png?w=1181&#038;h=1200&#038;fit=crop&#038;crop=faces%2Cfocalpoint 1181w, https:\/\/assets.science.nasa.gov\/dynamicimage\/assets\/science\/missions\/landsat\/data-pages\/Railroad-Valley-Nevada.png?w=1969&#038;h=2000&#038;fit=crop&#038;crop=faces%2Cfocalpoint 1969w\" sizes=\"auto, (max-width: 2678px) 100vw, 2678px\" \/><\/a><\/figure><figcaption class=\"hds-caption padding-y-2\">\n<div class=\"hds-caption-text p-sm margin-0\">A natural-color Landsat 9 image of Railroad Valley Playa in Nevada, acquired on June 29, 2024. A portion of the playa is used as a radiometric calibration and validation site for various satellite sensors including Landsat 8 and 9\u2019s OLI instruments.<\/div>\n<div class=\"hds-credits\">NASA\/USGS<\/div>\n<\/figcaption><\/div>\n<\/div>\n<\/div>\n<p>The Cal\/Val team at NASA Goddard Space Flight Center works closely with the Landsat Flight Operations Team to plan weekly calibration activities to maintain the radiometric accuracy of Landsat products. In October 2025, a <a href=\"https:\/\/www.usgs.gov\/landsat-missions\/news\/landsat-9-returns-normal-operations-following-brief-safehold\" rel=\"noopener\">Landsat 9 anomaly<\/a> occurred related to its solar array drive assembly (SADA) potentiometer. The spacecraft and instruments were placed in a safehold, pausing data collections. The Cal\/Val team assessed the instruments after they recovered from this anomaly, including monitoring the instrument telemetry, detector gains, and noise performance. The team identified a mis-loaded detector map and updated the calibration of both the reflective and thermal emissive bands to ensure consistent, accurate data. After six days in the safehold, the instrument resumed normal operations.\u00a0<\/p>\n<p>The NASA Cal\/Val team supports their USGS counterparts with quarterly updates to the Calibration Parameter File (CPF) by providing inputs for relative and absolute gains as needed. This work involves collaborating with USGS scientists to ensure the consistency of the Combined Radiometric Model (CRaM). The CRaM approach integrates radiometric responses from on-board calibrators to enhance long-term calibration stability throughout mission lifetimes. The CRaM algorithm also provides an extensible framework for future satellite missions. A peer-reviewed publication detailing the CRaM\u2019s approach and future applications was submitted to <em>Science of Remote Sensing<\/em>.<\/p>\n<p>On January 14-16, 2025, the Landsat Cal\/Val team organized and hosted the first semiannual Technical Information Meeting (TIM) at NASA Goddard Space Flight Center. NASA and USGS scientists welcomed collaborating scientists from South Dakota State University (SDSU), the University of Arizona Tucson, and Rochester Institute of Technology for presentations and discussions on Landsat imaging performance, algorithms, and instrument health. On May 28-29, 2025, the Cal\/Val team attended the second semiannual TIM at SDSU.<\/p>\n<p>The Landsat Cal\/Val Team is validating the accuracy of the Harmonized Landsat and Sentinel-2 (HLS) v2.0 product, which combines data from multiple satellites to create a continuous record of Earth\u2019s surface reflectance measurements since 2013. The team is testing the dataset using RadCalNet, a global network of automated ground stations that provide precise, standardized measurements. The team compared measurements from four RadCalNet sites, including the well-established <a href=\"https:\/\/calval.cr.usgs.gov\/apps\/railroad-valley\" rel=\"noopener\">Railroad Valley Playa site in Nevada<\/a>, against near-simultaneous HLS data. Their analysis shows the satellite and ground measurements agree within expected uncertainty ranges\u2014a strong validation of the HLS product\u2019s accuracy.<\/p>\n<p>The team presented these findings at the CEOS IVOS calibration meeting in Tucson, Arizona (September 1-5, 2025) and is currently preparing a peer-reviewed article to share the complete results.<\/p>\n<h4 class=\"wp-block-heading\"><strong>Path Forward<\/strong><\/h4>\n<p>The Cal\/Val team applies lessons learned from Landsat missions to better plan calibration efforts for the next generation of instruments. Using instrument performance checklists from Landsat 8\/9, the team is building a framework of in-house geometric and radiometric testing and extending algorithms for future Landsat instruments.<\/p>\n<p>The Landsat Cal\/Val Team is actively tackling a critical challenge in solar irradiance modeling. While new hyperspectral sensor technologies have made it possible to create highly accurate solar models with much lower uncertainty, the remote sensing community still lacks agreed-upon methods for applying these advanced models. A dedicated subgroup within the Landsat Cal\/Val Team is now developing and testing standardized approaches to bridge this gap. Their goal is to create clear recommendations and best practices that the scientific community can refine together and implement consistently.<\/p>\n<p>This work addresses a fundamental need\u2014transforming promising hyperspectral solar modeling capabilities into practical, standardized tools that researchers can confidently use across different projects and applications.<\/p>\n<div id=\"\" class=\"nasa-gb-align-full width-full maxw-full padding-x-3 padding-y-0 hds-module hds-module-full alignfull wp-block-nasa-blocks-related-articles\">\n<section class=\"hds-related-articles padding-x-0 padding-y-3 desktop:padding-top-7 desktop:padding-bottom-9\">\n<div class=\"w-100 grid-row grid-container maxw-widescreen padding-0 text-align-left\">\n<div class=\"margin-bottom-4\">\n<h2 style=\"max-width: 100%;\" class=\"width-full w-full maxw-full\">Explore More<\/h2>\n<\/div>\n<\/div>\n<div class=\"grid-row grid-container maxw-widescreen padding-0\">\n<div class=\"grid-col-12 desktop:grid-col-4 margin-bottom-4 desktop:margin-bottom-0 desktop:padding-right-3\">\n\t\t\t\t\t\t<a href=\"https:\/\/science.nasa.gov\/missions\/landsat\/whats-next-for-hls\/\" class=\"color-carbon-black\" rel=\"noopener\"><\/p>\n<div class=\"margin-bottom-2\">\n<div class=\"hds-cover-wrapper cover-hover-zoom bg-carbon-black minh-mobile\">\n<figure class=\"hds-media-background  \"><img decoding=\"async\" width=\"3022\" height=\"2015\" src=\"https:\/\/assets.science.nasa.gov\/dynamicimage\/assets\/science\/esd\/eo\/images\/imagerecords\/153000\/153978\/canada_opera_2023_lrg.jpg?w=3022&#038;h=2015&#038;%23038;fit=clip&#038;%23038;crop=faces%2Cfocalpoint\" class=\"attachment-medium size-medium\" alt=\"\" style=\"transform: scale(1); 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transform-origin: 50% 50%; object-position: 50% 50%; object-fit: cover;\" block_context=\"nasa-block\" loading=\"lazy\" \/><\/figure>\n<\/p>\n<\/div>\n<\/div>\n<div class=\"padding-right-0 desktop:padding-right-10\">\n<div class=\"subheading margin-bottom-1\">4 min read<\/div>\n<div class=\"margin-bottom-1\">\n<h3 class=\"related-article-title\">Tool Uses NASA Data to Take Temperature of Rivers from Space<\/h3>\n<\/div>\n<p class=\"p-md color-carbon-60\">New research uses more than 40 years of data from NASA and the U.S. Geological\u2026<\/p>\n<div class=\"display-flex flex-align-center label related-article-label margin-bottom-1 color-carbon-60\">\n\t\t\t\t\t\t\t\t\t<span class=\"display-flex flex-align-center margin-right-2\"><br \/>\n\t\t\t\t\t\t\t\t\t\t<svg version=\"1.1\" class=\"square-2 margin-right-1\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" x=\"0px\" y=\"0px\" width=\"16px\" height=\"16px\" viewBox=\"0 0 16 16\" style=\"enable-background:new 0 0 16 16;\" xml:space=\"preserve\"><g><g><path d=\"M8,0C3.5,0-0.1,3.7,0,8.2C0.1,12.5,3.6,16,8,16c4.4,0,8-3.6,8-8C16,3.5,12.4,0,8,0z M8,15.2 C4,15.2,0.8,12,0.8,8C0.8,4,4,0.8,8,0.8c3.9,0,7.2,3.2,7.2,7.1C15.2,11.9,12,15.2,8,15.2z\"\/><path d=\"M5.6,12c0.8-0.8,1.6-1.6,2.4-2.4c0.8,0.8,1.6,1.6,2.4,2.4c0-2.7,0-5.3,0-8C8.8,4,7.2,4,5.6,4 C5.6,6.7,5.6,9.3,5.6,12z\"\/><\/g><\/g><\/svg><br \/>\n\t\t\t\t\t\t\t\t\t\t<span>Article<\/span><br \/>\n\t\t\t\t\t\t\t\t\t<\/span><br \/>\n\t\t\t\t\t\t\t\t\t<span class=\"\"><br \/>\n\t\t\t\t\t\t\t\t\t\t2 weeks ago\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t<\/div>\n<\/p>\n<\/div>\n<p>\t\t\t\t\t\t<\/a>\n\t\t\t\t\t<\/div>\n<\/p>\n<\/div>\n<\/section>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>The NASA CalVal team spent 2025 improving their calibration techniques, strengthening collaboration, and sharing their work with the remote sensing community. Looking ahead, they\u2019re applying lessons learned to prepare for future Landsat missions.<\/p>\n","protected":false},"author":13,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"om_disable_all_campaigns":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"_uf_show_specific_survey":0,"_uf_disable_surveys":false,"footnotes":""},"categories":[16159],"tags":[],"class_list":["post-331174","post","type-post","status-publish","format-standard","hentry","category-landsat"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/www.vibewire.com.au\/index.php?rest_route=\/wp\/v2\/posts\/331174","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.vibewire.com.au\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.vibewire.com.au\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.vibewire.com.au\/index.php?rest_route=\/wp\/v2\/users\/13"}],"replies":[{"embeddable":true,"href":"https:\/\/www.vibewire.com.au\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=331174"}],"version-history":[{"count":3,"href":"https:\/\/www.vibewire.com.au\/index.php?rest_route=\/wp\/v2\/posts\/331174\/revisions"}],"predecessor-version":[{"id":331317,"href":"https:\/\/www.vibewire.com.au\/index.php?rest_route=\/wp\/v2\/posts\/331174\/revisions\/331317"}],"wp:attachment":[{"href":"https:\/\/www.vibewire.com.au\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=331174"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.vibewire.com.au\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=331174"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.vibewire.com.au\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=331174"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}